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Foundations and Applications of Sensor Management [electronic resource] /edited by Alfred O. Hero, David A. Castañón, Douglas Cochran, Keith Kastella.

by Hero, Alfred O [editor.]; Castañón, David A [editor.]; Cochran, Douglas [editor.]; Kastella, Keith [editor.]; SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Boston, MA : Springer US, 2008.Description: online resource.ISBN: 9780387498195.Subject(s): Engineering | Coding theory | Telecommunication | Engineering | Signal, Image and Speech Processing | Control Engineering | Coding and Information Theory | Communications Engineering, Networks | Electronic and Computer EngineeringDDC classification: 621.382 Online resources: Click here to access online
Contents:
Overview of Book -- Stochastic Control Theory for Sensor Management -- Information Theoretic Approaches to Sensor Management -- Joint Multi-Target Particle Filtering -- Pomdp Approximation Using Simulation and Heuristics -- Multi-Armed Bandit Problems -- Application of Multi-Armed Bandits to Sensor Management -- Active Learning and Sampling -- Plan-In-Advance Active Learning 0f Classifiers -- Application of Sensor Scheduling Concepts to Radar -- Defense Applications -- Appendices.
In: Springer eBooksSummary: Foundations and Applications of Sensor Management presents the emerging theory of sensor management with applications to real-world examples such as landmine detection, adaptive signal and image sampling, multi-target tracking, and radar waveform scheduling. It is written by leading experts in the field for a diverse engineering audience ranging from signal processing, to automatic control, statistics, and machine learning. The level of treatment of the book is tutorial and self-contained. The chapters of the book follow a logical development from theoretical foundations to approximate approaches and ending with applications. The coverage includes the following topics: stochastic control foundations of sensor management; multi-armed bandits and their connections to sensor management; information-theoretic approaches; managed sensing for multi-target tracking; approximation methods based on embedded simulation; active learning for classification and sampling; and waveform scheduling for radar. An appendix is included to provide essential background on topics the reader may not have encountered as a first-year graduate student: Markov decision processes; information theory; and stopping times. Foundations and Applications of Sensor Management is an important reference for signal processing and control engineers and researchers as well as machine learning application developers.
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Overview of Book -- Stochastic Control Theory for Sensor Management -- Information Theoretic Approaches to Sensor Management -- Joint Multi-Target Particle Filtering -- Pomdp Approximation Using Simulation and Heuristics -- Multi-Armed Bandit Problems -- Application of Multi-Armed Bandits to Sensor Management -- Active Learning and Sampling -- Plan-In-Advance Active Learning 0f Classifiers -- Application of Sensor Scheduling Concepts to Radar -- Defense Applications -- Appendices.

Foundations and Applications of Sensor Management presents the emerging theory of sensor management with applications to real-world examples such as landmine detection, adaptive signal and image sampling, multi-target tracking, and radar waveform scheduling. It is written by leading experts in the field for a diverse engineering audience ranging from signal processing, to automatic control, statistics, and machine learning. The level of treatment of the book is tutorial and self-contained. The chapters of the book follow a logical development from theoretical foundations to approximate approaches and ending with applications. The coverage includes the following topics: stochastic control foundations of sensor management; multi-armed bandits and their connections to sensor management; information-theoretic approaches; managed sensing for multi-target tracking; approximation methods based on embedded simulation; active learning for classification and sampling; and waveform scheduling for radar. An appendix is included to provide essential background on topics the reader may not have encountered as a first-year graduate student: Markov decision processes; information theory; and stopping times. Foundations and Applications of Sensor Management is an important reference for signal processing and control engineers and researchers as well as machine learning application developers.

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